• Energy Research

The electric vehicle technology is one of the most promising candidates to reduce fuel consumption and CO2 emission. Although electric vehicles have been widely promoted by governments around the world, their development is seriously hampered due to charger unavailability and range anxiety. Based on this, this paper designs an energy interaction converter between two electric vehicles, which is controlled through disturbance observer based sliding model control algorithm. For this converter, three main demands should be satisfied, i.e., high power density, weak source and constant power load. Firstly, the equivalent impedance switching process is introduced to eliminate the impact of weak source. Meanwhile, the equivalent six channel interleaved floating dual boost converter is chosen to satisfy the high power density demand, whose generalized state-space function is further built to provide an indispensable preprocessing for following controller design. Moreover, in order to solve the problem regarding low frequency/sub-synchronous oscillation caused through constant power load feature regarding the energy consumption vehicle and weak source feature regarding the energy supply vehicle, a disturbance observer based sliding model control algorithm is proposed through using generalized state-space function to provide standard DC power with both constant voltage and power. Furthermore, the proportional-resonant controller is proposed to solve the current sharing problem among six parallel channels, which reduces the heat loss and improves the service life of the device. Finally, simulation and experimental results verify the high performance of the proposed control algorithm.

In this paper, we present maximum power point tracking for a wind power plant (WPP) using the gradient ascent (GA) in a data-driven manner. The conventional GA method achieves fast convergent performance by considering only direct wake terms when calculating the axial induction factors. However, the conventional method might not be close to optimal even when the wind conditions are steady state. In this paper, we propose a new method using the relationships between the direct and indirect wake terms. Using the relationship between the wake terms can prevent sudden deviations after convergence to a single operating point, even when significant indirect wake terms exist in the presence of multiple wakes. Therefore, the proposed method provides not only fast convergence to an operating point, but also closer-to-optimal power production without sudden deviations compared to the conventional method. We validated the effectiveness of the proposed method using modeled WPP layouts with various wind conditions.

This paper develops dynamic economic dispatch (DED) using model predictive control (MPC) based on Weibull probabilistic distribution function (PDF). Firstly we performed economic dispatch (ED) based onWeibull PDF.We considered the probabilistic cost about over-estimated and under-estimated cost of wind power by using Weibull PDF. Secondly we implemented DED formulated in a MPC framework using the maximum wind power capability from the ED results since ED does not consider the ramp rate constraint. By using dispatch error cost, we could consider the current status of reliable generation by using the proposed MPC method. It turns out that the DED using MPC method is very effective in coping with intermittent resource such as wind variability.

As the wind power penetration level increases, the inertial response of a wind power plant (WPP) is important to the grid under load and generation imbalance. In this paper, we present coordinated WPP control for frequency support under wake effects. By de-loading the power from a WPP, the inertial response can be obtained from the kinetic energy of wind turbine rotors. The coordinated WPP control considering wake effects improves the inertial response of WPP by preventing the rotor speed recovery period, which is unfavorable during a frequency drop. The proposed frequency support scheme could prevent large generation reduction right after an inertial response. In addition, more power generation is available when a WPP uses both primary and secondary control. To validate the performance, a WPP with three wind turbines was used as a case study with wake, turbine, and generator models.

In this paper, a nonlinear control method is proposed to improve the primary frequency control for doubly fed induction generator (DFIG) wind turbine system. Individual wind turbines in wind power plant are controlled in deloaded operation to provide the frequency support during depressed frequency conditions. By considering of nonlinearity of the system, input-output linearization method is applied to improve the performance of primary frequency response. It can be guaranteed that the system is exponentially stable with the proposed method. The proposed method is validated by using a simple microgrid, consisting of one wind power plant, one diesel generator, and a load, through MATLAB/Simulink. The simulation results show that the proposed method has an improvement of primary frequency control when the load increases.

This paper presents a passivity-based control (PBC) for a type 2 static synchronous compensator (STATCOM) based on an Euler-Lagrange model. The proposed method is designed based on a Lyapunov function by considering dissipation to improve transient performance. An additional nonlinear damping term is designed to regulate the capacitor voltage. It is guaranteed that the equilibrium point of the system is locally exponentially stable in the operating range. The performance of the proposed method is validated via a 100 Mvar STATCOM system connected to the 345-kV grid system in SimPowerSystems, MATLAB/Siumlink. To compare with the input-output feedback linearization method, the proposed control method has improved convergence and reduction of oscillations of the active current and DC voltage, as shown in simulation results. Lastly, it shows that the proposed method is robust to model mismatches as the variation of the grid voltage and the degradation of the capacitance.

This paper presents centralized control for a wind farm using model predictive control (MPC) method. In order to solve intra-day unit commitment (UC) problem, the UC problem is solved by using the MPC method with short-term wind power forecasting. We introduce a new dynamics model for the UC with some constraints to utilize the benefits of the MPC method. The objective function considering the operation and maintenance costs is formulated by adding a new variable in order to average the operating time of each wind turbine (WT) within the whole time. The proposed method could solve the UC problems on-line using the prediction time horizon that could be selected flexibly considering time horizon based on wind power forecasting errors. From the simulation study using 10 WTs, we observed that the proposed dynamics model of UC effectively provided the optimal solution to each scenario. Numerical study will validate that the proposed method can be applied to solving the UC problem of a large scale wind farm by aggregating WTs.

The integration of various grid-enhancing technologies is crucial for addressing the current challenges faced in controlling transmission grids. The growing variability in energy supply necessitates innovative solutions such as advanced grid monitoring and control systems, energy storage technologies, and flexible grid infrastructure. These technologies are essential for balancing the intermittent nature of renewable energy sources (RESs) and ensuring a stable and reliable electricity supply. This paper offers a comprehensive review of grid-enhancing technologies, incorporating insights from worldwide academic papers and various existing industrial projects that address current challenges and enhance the functionality, reliability, and sustainability of transmission grids. The findings highlight that modernizing grid infrastructure and integrating cutting-edge digital technologies are vital for effectively incorporating a growing number of RESs and enhancing overall grid resilience and efficiency. In addition, the paper explores future opportunities for improving the control of transmission grids, emphasizing the need for continued innovation and investment to meet evolving energy demands and support the transition to a more sustainable energy system.

The emerging novel energy infrastructures, such as energy communities, smart building-based microgrids, electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system. It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters. In order to efficiently, optimally, and flexibly control multi-microgrid clusters, cross-disciplinary technologies such as power electronics, control theory, optimization algorithms, information and communication technologies, cyber-physical, and big-data analysis are needed. This paper introduces an overview of the relevant aspects for multi-microgrids, including the outstanding features, architectures, typical applications, existing control mechanisms, as well as the challenges.